Conciseness

The body is efficient: no explanation of concepts Claude already knows, and every command block carries real information. Minor trim candidates keep it from 5 — the inline "Interpretation Rules" thresholds duplicate references/outcome_tags.md, and the "Shared FMP client" section reads like an implementation changelog (retry counts, key masking, v3 fallback policy) that could be tightened or moved to the reference layer.

Actionability

Fully executable, copy-paste-ready commands cover the common cases: ingest (with --include-rejects guidance), update in both FMP and offline --prices-json modes with --horizons 3,5, and summarize with concrete --group-by and --min-sample values, plus enumerated record fields and output filenames. Matches the fully-executable anchor since both data-source modes are explicitly shown.

Workflow Clarity

Four clearly sequenced steps each carry their command, and most checkpoints are present: "Review the generated Markdown and JSON reports", "Treat rule_candidates as evidence prompts, not automatic rule changes", "Inspect representative charts manually before changing trade rules", and documented failure semantics (update exits 1 and leaves the model book unchanged on total transport failure). Not 5 because there is no post-ingest verification step and no error-recovery guidance for the exit-1 case.

Progressive Disclosure

A clear overview with well-signaled, one-level-deep references: references/model_book_schema.md, references/outcome_tags.md, and references/review_workflow.md all exist, none reference further files, and the Resources section describes each one's purpose. Schema detail, tag conditions, and daily/weekly/monthly routines are appropriately split out, with only compact summaries kept inline; bundled scripts (build_model_book.py, fmp_client.py) are correctly referenced rather than inlined.

Specificity

"Build a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics" lists multiple concrete actions (build, update, tag, summarize) with named, domain-specific outputs. Matches the comprehensive-coverage anchor rather than the minor-gaps anchor because both the inputs (screener candidates) and every processing stage are named specifically.

Completeness

Explicitly answers both questions: the first sentence states concrete capabilities (build model book, update forward outcomes with MFE/MAE/stop-hit/tags/cohort stats) and the second gives an explicit "Use when the user wants to…" clause with five concrete trigger phrases. Clearly matches the both-what-and-when-with-concrete-triggers anchor, not the merely-explicit-when anchor at 4.

Trigger Term Quality

Trigger phrases like "study Stockbee Momentum Burst examples", "track failed candidates", "build setup fluency", and "review A/B setup quality" are natural for this domain and give good keyword coverage. Not 5 because coverage is somewhat jargon-bound and misses common variations a user might say (e.g., reviewing trades, win rates, or backtesting screener picks).

Distinctiveness Conflict Risk

The Stockbee momentum-burst niche, the screener-candidate input, and the explicit positioning as "a learning loop rather than immediate trade signals" give it distinct triggers with minimal conflict risk, including against the sibling screener skill. Third-person/imperative voice is used throughout, so no voice penalty applies.